This essay is the tenth chapter in Franco Cortese’s forthcoming e-book, I Shall Not Go Quietly Into That Good Night!: My Quest to Cure Death, published by the Center for Transhumanity. The first nine chapters were previously published on The Rational Argumentator under the following titles:

One of the reasons for continuing conceptual development of the physical-functionalist NRU (neuron-replication-unit) approach, despite the perceived advantages of the informational-functionalist approach, was in the event that computational emulation would either fail to successfully replicate a given physical process (thus a functional-modality concern) or fail to successfully maintain subjective-continuity (thus an operational-modality concern), most likely due to a difference in the physical operation of possible computational substrates compared to the physical operation of the brain (see Chapter 2). In regard to functionality, we might fail to computationally replicate (whether in simulation or emulation) a relevant physical process for reasons other than vitalism. We could fail to understand the underlying principles governing it, or we might understand its underlying principles so as to predictively model it yet still fail to understand how it affects the other processes occurring in the neuron—for instance if we used different modeling techniques or general model types to model each component, effectively being able to predictively model each individually while being unable to model how they affect eachother due to model untranslatability. Neither of these cases precludes the aspect in question from being completely material, and thus completely potentially explicable using the normative techniques we use to predictively model the universe. The physical-functionalist approach attempted to solve these potential problems through several NRU sub-classes, some of which kept certain biological features and functionally replaced certain others, and others that kept alternate biological features and likewise functionally replicated alternate biological features. These can be considered as varieties of biological-nonbiological NRU hybrids that functionally integrate those biological features into their own, predominantly non-biological operation, as they exist in the biological nervous system, which we failed to functionally or operationally replicate successfully.

The subjective-continuity problem, however, is not concerned with whether something can be functionally replicated but with whether it can be functionally replicated while still retaining subjective-continuity throughout the procedure.

This category of possible basis for subjective-continuity has stark similarities to the possible problematic aspects (i.e., operational discontinuity) of current computational paradigms and substrates discussed in Chapter 2. In that case it was postulated that discontinuity occurred as a result of taking something normally operationally continuous and making it discontinuous: namely, (a) the fact that current computational paradigms are serial (whereas the brain has massive parallelism), which may cause components to only be instantiated one at a time, and (b) the fact that the resting membrane potential of biological neurons makes them procedurally continuous—that is, when in a resting or inoperative state they are still both on and undergoing minor fluctuations—whereas normative logic gates both do not produce a steady voltage when in an inoperative state (thus being procedurally discontinuous) and do not undergo minor fluctuations within such a steady-state voltage (or, more generally, a continuous signal) while in an inoperative state. I had a similar fear in regard to some mathematical and computational models as I understood them in 2009: what if we were taking what was a continuous process in its biological environment, and—by using multiple elements or procedural (e.g., computational, algorithmic) steps to replicate what would have been one element or procedural step in the original—effectively making it discontinuous by introducing additional intermediate steps? Or would we simply be introducing a number of continuous steps—that is, if each element or procedural step were operationally continuous in the same way that the components of a neuron are, would it then preserve operational continuity nonetheless?

This led to my attempting to develop a modeling approach aiming to retain the same operational continuity as exists in biological neurons, which I will call the relationally isomorphic mathematical model. The biophysical processes comprising an existing neuron are what implements computation; by using biophysical-mathematical models as our modeling approach, we might be introducing an element of discontinuity by mathematically modeling the physical processes giving rise to a computation/calculation, rather than modeling the computation/calculation directly. It might be the difference between modeling a given program, and the physical processes comprising the logic elements giving rise to the program. Thus, my novel approach during this period was to explore ways to model this directly.

Rather than using a host of mathematical operations to model the physical components that themselves give rise to a different type of mathematics, we instead use a modeling approach that maintains a 1-to-1 element or procedural-step correspondence with the level-of-scale that embodies the salient (i.e., aimed-for) computation. My attempts at developing this produced the following approach, though I lack the pure mathematical and computer-science background to judge its true accuracy or utility. The components, their properties, and the inputs used for a given model (at whatever scale) are substituted by numerical values, the magnitude of which preserves the relationships (e.g., ratio relationships) between components/properties and inputs, and by mathematical operations which preserve the relationships exhibited by their interaction. For instance: if the interaction between a given component/property and a given input produces an emergent inhibitory effect biologically, then one would combine them to get their difference or their factors, respectively, depending on whether they exemplify a linear or nonlinear relationship. If the component/property and the input combine to produce emergently excitatory effects biologically, one would combine them to get their sum or products, respectively, depending on whether they increased excitation in a linear or nonlinear manner.

In an example from my notes, I tried to formulate how a chemical synapse could be modeled in this way. Neurotransmitters are given analog values such as positive or negative numbers, the sign of which (i.e., positive or negative) depends on whether it is excitatory or inhibitory and the magnitude of which depends on how much more excitatory/inhibitory it is than other neurotransmitters, all in reference to a baseline value (perhaps 0 if neutral or neither excitatory nor inhibitory; however, we may need to make this a negative value, considering that the neuron’s resting membrane-potential is electrically negative, and not electrochemically neutral). If they are neurotransmitter clusters, then one value would represent the neurotransmitter and another value its quantity, the sum or product of which represents the cluster. If the neurotransmitter clusters consist of multiple neurotransmitters, then two values (i.e., type and quantity) would be used for each, and the product of all values represents the cluster. Each summative-product value is given a second vector value separate from its state-value, representing its direction and speed in the 3D space of the synaptic junction. Thus by summing the products of all, the numerical value should contain the relational operations each value corresponds to, and the interactions and relationships represented by the first- and second-order products. The key lies in determining whether the relationship between two elements (e.g., two neurotransmitters) is linear (in which case they are summed), or nonlinear (in which case they are combined to produce a product), and whether it is a positive or negative relationship—in which case their factor, rather than their difference, or their product, rather than their sum, would be used. Combining the vector products would take into account how each cluster’s speed and position affects the end result, thus effectively emulating the process of diffusion across the synaptic junction. The model’s past states (which might need to be included in such a modeling methodology to account for synaptic plasticity—e.g., long-term potentiation and long-term modulation) would hypothetically be incorporated into the model via a temporal-vector value, wherein a third value (position along a temporal or “functional”/”operational” axis) is used when combining the values into a final summative product. This is similar to such modeling techniques as phase-space, which is a quantitative technique for modeling a given system’s “system-vector-states” or the functional/operational states it has the potential to possess.

How excitatory or inhibitory a given neurotransmitter is may depend upon other neurotransmitters already present in the synaptic junction; thus if the relationship between one neurotransmitter and another is not the same as that first neurotransmitter and an arbitrary third, then one cannot use static numerical values for them because the sequence in which they were released would affect how cumulatively excitatory or inhibitory a given synaptic transmission is.

A hypothetically possible case of this would be if one type of neurotransmitter can bond or react with two or more types of neurotransmitter. Let’s say that it’s more likely to bond or react with one than with the other. If the chemically less attractive (or reactive) one were released first, it would bond anyways due to the absence of the comparatively more chemically attractive one, such that if the more attractive one were released thereafter, then it wouldn’t bond because the original one would have already bonded with the chemically less attractive one.

If a given neurotransmitter’s numerical value or weighting is determined by its relation to other neurotransmitters (i.e., if one is excitatory, and another is twice as excitatory, then if the first was 1.5, the second would be 3—assuming a linear relationship), and a given neurotransmitter does prove to have a different relationship to one neurotransmitter than it does another, then we cannot use a single value for it. Thus we might not be able to configure it such that the normative mathematical operations follow naturally from each other; instead, we may have to computationally model (via the [hypothetically] subjectively discontinuous method that incurs additional procedural steps) which mathematical operations to perform, and then perform them continuously without having to stop and compute what comes next, so as to preserve subjective-continuity.

We could also run the subjectively discontinuous model at a faster speed to account for its higher quantity of steps/operations and the need to keep up with the relationally isomorphic mathematical model, which possesses comparatively fewer procedural steps. Thus subjective-continuity could hypothetically be achieved (given the validity of the present postulated basis for subjective-continuity—operational continuity) via this method of intermittent external intervention, even if we need extra computational steps to replicate the single informational transformations and signal-combinations of the relationally isomorphic mathematical model.

This essay is the ninth chapter in Franco Cortese’s forthcoming e-book, I Shall Not Go Quietly Into That Good Night!: My Quest to Cure Death, published by the Center for Transhumanity. The first eight chapters were previously published on The Rational Argumentator under the following titles:

The two approaches falling within this class considered thus far are (a) computational models that model the biophysical (e.g., electromagnetic, chemical, and kinetic) operation of the neurons—i.e., the physical processes instantiating their emergent functionality, whether at the scale of tissues, molecules and/or atoms, and anything in between—and (b) abstracted models, a term which designates anything that computationally models the neuron using the (sub-neuron but super-protein-complex) components themselves as the chosen model-scale (whereas the latter uses for its chosen model-scale the scale at which physical processes emergently instantiating those higher-level neuronal components exist, such as the membrane and individual proteins forming the transmembrane protein-complexes), regardless of whether each component is abstracted as a normative-electrical-component analogue (i.e., using circuit diagrams in place of biological schematics, like equating the lipid bilayer membrane with a capacitor connected to a variable battery) or mathematical models in which a relevant component or aspect of the neuron becomes a term (e.g., a variable or constant) in an equation.

It was during the process of trying to formulate different ways of mathematically (and otherwise computationally) modeling neurons or sub-neuron regions that I laid the conceptual embryo of the first new possible basis for subjective-continuity: the notion of operational isomorphism.

A New Approach to Subjective-Continuity Through Substrate Replacement

There are two other approaches to increasing the likelihood of subjective-continuity, each based on the presumption of two possible physical bases for discontinuity, that I explored during this period. Note that these approaches are unrelated to graduality, which has been the main determining factor impacting the likelihood of subjective-continuity considered thus far. The new approaches consist of designing the NRUs so as to retain the respective postulated physical bases for subjective-continuity that exist in the biological brain. Thus they are unrelated to increasing the efficacy of the gradual-replacement procedure itself, instead being related to the design requirements of functional-equivalents used to gradually replace the neurons that maintain immediate subjective-continuity.

Operational Isomorphism

Whereas functionality deals only with the emergent effects or end-product of a given entity or process, operationality deals with the procedural operations performed so as to give rise to those emergent effects. A mathematical model of a neuron might be highly functionally equivalent while failing to be operationally equivalent in most respects. Isomorphism can be considered a measure of “sameness”, but technically means a 1-to-1 correspondence between the elements of two sets (which would correspond with operational isomorphism) or between the sums or products of the elements of two sets (which would correspond with functional isomorphism, using the definition of functionality employed above). Thus, operational isomorphism is the degree with which the sub-components (be they material as in entities or procedural as in processes) of the two larger-scale components, or the operational modalities possessed by each respective collection of sub-components, are equivalent.

To what extent does the brain possess operational isomorphism? It seems to depend on the scale being considered. At the highest scale, different areas of the nervous system are classed as systems (as in functional taxonomies) or regions (as in anatomical taxonomies). At this level the separate regions (i.e., components of a shared scale) differ widely from one another in terms of operational-modality; they process information very differently from the way other components on the same scale process information. If this scale was chosen as the model-scale of our replication-approach and the preceding premise (that the physical basis for subjective-continuity is the degree of operational isomorphism between components at a given scale) is accepted, then we would in such a case have a high probability of replicating functionality, but a low probability of retaining subjective-continuity through gradual replacement. This would be true even if we used the degree of operational isomorphism between separate components as the only determining factor for subjective-continuity, and ignored concerns of graduality (e.g., the scale or rate—or scale-to-rate ratio—at which gradual substrate replacement occurs).

Contrast this to the molecular scale, where the operational modality of each component (being a given molecule) and the procedural rules determining the state-changes of components at this scale are highly isomorphic. The state-changes of a given molecule are determined by molecular and atomic forces. Thus if we use an informational-functionalist approach, choose a molecular scale for our model, and accept the same premises as the first example, we would have a high probability of both replicating functionality and retaining subjective-continuity through gradual replacement because the components (molecules) have a high degree of operational isomorphism.

Note that this is only a requirement for the sub-components instantiating the high-level neural regions/systems that embody our personalities and higher cognitive faculties such as the neocortex — i.e., we wouldn’t have to choose a molecular scale as our model scale (if it proved necessary for the reasons described above) for the whole brain, which would be very computationally intensive.

So at the atomic and molecular scale the brain possesses a high degree of operational isomorphism. On the scale of the individual protein complexes, which collectively form a given sub-neuronal component (e.g., ion channel), components still appear to possess a high degree of operational isomorphism because all state-changes are determined by the rules governing macroscale proteins and protein-complexes (i.e., biochemistry and particularly protein-protein interactions); by virtue of being of the same general constituents (amino acids), the factors determining state-changes at this level are shared by all components at this scale. The scale of individual neuronal components, however, seems to possess a comparatively lesser degree of operational isomorphism. Some ion channels are ligand-gated while others are voltage-gated. Thus, different aspects of physicality (i.e., molecular shape and voltage respectively) form the procedural-rules determining state-changes at this scale. Since there are now two different determining factors at this scale, its degree of operational isomorphism is comparatively less than the protein and protein-complex scale and the molecular scale, both of which appear to have only one governing procedural-rule set. The scale of individual neurons by contrast appears to possess a greater degree of operational isomorphism; every neuron fires according to its threshold value, and sums analog action-potential values into a binary output (i.e., neuron either fires or does not). All individual neurons operate in a highly isomorphic manner. Even though individual neurons of a given type are more operationally isomorphic in relation to each other than with a neuron of another type, all neurons regardless of type still act in a highly isomorphic manner. However, the scale of neuron-clusters and neural-networks, which operate and communicate according to spatiotemporal sequences of firing patterns (action-potential patterns), appears to possess a lesser degree of operational isomorphism compared to individual neurons, because different sequences of firing patterns will mean a different thing to two respective neural clusters or networks. Also note that at this scale the degree of functional isomorphism between components appears to be less than their degree of operational isomorphism—that is, the way each cluster or network operates is more similar in relation to each other than is their actual function (i.e., what they effectively do). And lastly, at the scale of high-level neural regions/systems, components (i.e., neural regions) differ significantly in morphology, in operationality, and in functionality; thus they appear to constitute the scale that possesses the least operational isomorphism.

I will now illustrate the concept of operational isomorphism using the physical-functionalist and the informational-functionalist NRU approaches, respectively, as examples. In terms of the physical-functionalist (i.e., prosthetic neuron) approach, both the passive (i.e., “direct”) and CPU-controlled sub-classes, respectively, are operationally isomorphic. An example of a physical-functionalist NRU that would not possess operational isomorphism is one that uses a passive-physicalist approach for the one type of component (e.g., voltage-gated ion channel) and a CPU-controlled/cyber-physicalist approach [see Part 4 of this series] for another type of component (e.g., ligand-gated ion channel)—on that scale the components act according to different technological and methodological infrastructures, exhibit different operational modalities, and thus appear to possess a low degree of operational isomorphism. Note that the concern is not the degree of operational isomorphism between the functional-replication units and their biological counterparts, but rather with the degree of operational isomorphism between the functional-replication units and other units on the same scale.

Another possibly relevant type of operational isomorphism is the degree of isomorphism between the individual sub-components or procedural-operations (i.e., “steps”) composing a given component, designated here as intra-operational isomorphism. While very similar to the degree of isomorphism for the scale immediately below, this differs from (i.e., is not equivalent to) such a designation in that the sub-components of a given larger component could be functionally isomorphic in relation to each other without being operationally isomorphic in relation to all other components on that scale. The passive sub-approach of the physical-functionalist approach would possess a greater degree of intra-operational isomorphism than would the CPU-controlled/cyber-physicalist sub-approach, because presumably each component would interact with the others (via physically embodied feedback) according to the same technological and methodological infrastructure—be it mechanical, electrical, chemical, or otherwise. The CPU-controlled sub-approach by contrast would possess a lesser degree of intra-operational-isomorphism, because the sensors, CPU, and the electric or electromechanical systems, respectively (the three main sub-components for each singular neuronal component—e.g., an artificial ion channel), operate according to different technological and methodological infrastructures and thus exhibit alternate operational modalities in relation to eachother.

In regard to the informational-functionalist approach, an NRU model that would be operationally isomorphic is one wherein, regardless of the scale used, the type of approach used to model a given component on that scale is as isomorphic with the ones used to model other components on the same scale as is possible. For example, if one uses a mathematical model to simulate spiking regions of the dendritic spine, then one shouldn’t use a non-mathematical (e.g., strict computational-logic) approach to model non-spiking regions of the dendritic spine. Since the number of variations to the informational-functionalist approach is greater than could exist for the physical-functionalist approach, there are more gradations to the degree of operational isomorphism. Using the exact same branches of mathematics to mathematically model the two respective components would incur a greater degree of operational isomorphism than if we used alternate mathematical techniques from different disciplines to model them. Likewise, if we used different computational approaches to model the respective components, then we would have a lesser degree of operational isomorphism. If we emulated some components while merely simulating others, we would have a lesser degree of operational isomorphism than if both were either strictly simulatory or strictly emulatory.

If this premise proves true, it suggests that when picking the scale of our replication-approach (be it physical-functionalist or informational-functionalist), we choose a scale that exhibits operational isomorphism—for example, the molecular scale rather than the scale of high-level neural-regions, and that we don’t use widely dissimilar types of modeling techniques to model one component (e.g., a molecular system) than we do for another component on the same scale.

Note that unlike operational-continuity, the degree of operational isomorphism was not an explicit concept or potential physical basis for subjective-continuity at the time of my working on immortality (i.e., this concept wasn’t yet fully fleshed out in 2010), but rather was formulated in response to going over my notes from this period so as to distill the broad developmental gestalt of my project; though it appears to be somewhat inherent (i.e., appears to be hinted at), it hasn’t been explicitized until relatively recently.

The next chapter describes the rest of my work on technological approaches to techno-immortality in 2010, focusing on a second new approach to subjective-continuity through a gradual-substrate-replacement procedure, and concluding with an overview of the ways my project differs from the other techno-immortalist projects.

This essay is the eighth chapter in Franco Cortese’s forthcoming e-book, I Shall Not Go Quietly Into That Good Night!: My Quest to Cure Death, published by the Center for Transhumanity. The first seven chapters were previously published on The Rational Argumentator under the following titles:

By 2009 I felt the major classes of physicalist-functionalist replication approaches to be largely developed, producing now only potential minor variations in approach and procedure. These developments consisted of contingency plans in the case that some aspect of neuronal operation couldn’t be replicated with alternate, non-biological physical systems and processes, based around the goal of maintaining those biological (or otherwise organic) systems and processes artificially and of integrating them with the processes that could be reproduced artificially.

2009 also saw further developments in the computational approach, where I conceptualized a new sub-division in the larger class of the informational-functionalist (i.e., computational, which encompasses both simulation and emulation) replication approach, which is detailed in the next chapter.

Developments in the Physicalist Approach

During this time I explored mainly varieties of the cybernetic-physical functionalist approach. This involved the use of replicatory units that preserve certain biological aspects of the neuron while replacing certain others with functionalist replacements, and other NRUs that preserved alternate biological aspects of the neuron while replacing different aspects with functional replacements. The reasoning behind this approach was twofold. The first was that there was a chance, no matter how small, that we might fail to sufficiently replicate some relevant aspect(s) of the neuron either computationally or physically by failing to understand the underlying principles of that particular sub-process/aspect. The second was to have an approach that would work in the event that there was some material aspect that couldn’t be sufficiently replicated via non-biological physically embodied systems (i.e., the normative physical-functionalist approach).

However, these varieties were conceived of in case we couldn’t replicate certain components successfully (i.e., without functional divergence). The chances of preserving subjective-continuity in such circumstances are increased by the number of varieties we have for this class of model (i.e., different arrangements of mechanical replacement components and biological components), because we don’t know which we would fail to functionally replicate.

This class of physical-functionalist model can be usefully considered as electromechanical-biological hybrids, wherein the receptors (i.e., transporter proteins) on the post-synaptic membrane are integrated with the artificial membrane and in coexistence with artificial ion-channels, or wherein the biological membrane is retained while the receptor and ion-channels are replaced with functional equivalents instead. The biological components would be extracted from the existing biological neurons and reintegrated with the artificial membrane. Otherwise they would have to be synthesized via electromechanical systems, such as, but not limited to, the use of chemical stores of amino-acids released in specific sequences to facilitate in vivo protein folding and synthesis, which would then be transported to and integrated with the artificial membrane. This is better than providing stores of pre-synthesized proteins, due to more complexities in storing synthesized proteins without decay or functional degradation over storage-time, and in restoring them from their “stored”, inactive state to a functionally-active state when they were ready for use.

During this time I also explored the possibility of using the neuron’s existing protein-synthesis systems to facilitate the construction and gradual integration of the artificial sections with the existing lipid bilayer membrane. Work in synthetic biology allows us to use viral gene vectors to replace a given cell’s constituent genome—and consequently allowing us to make it manufacture various non-organic substances in replacement of the substances created via its normative protein-synthesis. We could use such techniques to replace the existing protein-synthesis instructions with ones that manufacture and integrate the molecular materials constituting the artificial membrane sections and artificial ion-channels and ion-pumps. Indeed, it may even be a functional necessity to gradually replace a given neuron’s protein-synthesis machinery with protein-synthesis-based machinery for the replacement, integration and maintenance of the non-biological sections’ material, because otherwise those parts of the neuron would still be trying to rebuild each section of lipid bilayer membrane we iteratively remove and replace. This could be problematic, and so for successful gradual replacement of single neurons, a means of gradually switching off and/or replacing portions of the cell’s protein-synthesis systems may be required.

This essay is the seventh chapter in Franco Cortese’s forthcoming e-book, I Shall Not Go Quietly Into That Good Night!: My Quest to Cure Death, published by the Center for Transhumanity. The first six chapters were previously published on The Rational Argumentator under the following titles:

I was planning on using the NEMS already conceptually developed by Robert Freitas for nanosurgery applications (to be supplemented by the use of MEMS if the technological infrastructure was unavailable at the time) to take in vivo recordings of the salient neural metrics and properties needing to be replicated. One novel approach was to design the units with elongated, worm-like bodies, disposing the computational and electromechanical apparatus within the elongated body of the unit. This sacrifices width for length so as to allow the units to fit inside the extra-cellular space between neurons and glial cells as a postulated solution to a lack of sufficient miniaturization. Moreover, if a unit is too large to be used in this way, extending its length by the same proportion would allow it to then operate in the extracellular space, provided that its means of data-measurement itself weren’t so large as to fail to fit inside the extracellular space (the span of ECF between two adjacent neurons for much of the brain is around 200 Angstroms).

I was planning on using the chemical and electrical sensing methodologies already in development for nanosurgery as the technological and methodological infrastructure for the neuronal data-measurement methodology. However, I also explored my own conceptual approaches to data-measurement. This consisted of detecting variation of morphological features in particular, as the schemes for electrical and chemical sensing already extant seemed either sufficiently developed or to be receiving sufficient developmental support and/or funding. One was the use of laser-scanning or more generally radiography (i.e., sonar) to measure and record morphological data. Another was a device that uses a 2D array of depressible members (e.g., solid members attached to a spring or ratchet assembly, which is operatively connected to a means of detecting how much each individual member is depressed—such as but not limited to piezoelectric crystals that produce electricity in response and proportion to applied mechanical strain). The device would be run along the neuronal membrane and the topology of the membrane would be subsequently recorded by the pattern of depression recordings, which are then integrated to provide a topographic map of the neuron (e.g., relative location of integral membrane components to determine morphology—and magnitude of depression to determine emergent topology). This approach could also potentially be used to identify the integral membrane proteins, rather than using electrical or chemical sensing techniques, if the topologies of the respective proteins are sufficiently different as to be detectable by the unit (determined by its degree of precision, which typically is a function of its degree of miniaturization).

The constructional and data-measurement units would also rely on the technological and methodological infrastructure for organization and locomotion that would be used in normative nanosurgery. I conceptually explored such techniques as the use of a propeller, the use of pressure-based methods (i.e., a stream of water acting as jet exhaust would in a rocket), the use of artificial cilia, and the use of tracks that the unit attaches to so as to be moved electromechanically, which decreases computational intensiveness – a measure of required computation per unit time – rather than having a unit compute its relative location so as to perform obstacle-avoidance and not, say, damage in-place biological neurons. Obstacle-avoidance and related concerns are instead negated through the use of tracks that limit the unit’s degrees of freedom—thus preventing it from having to incorporate computational techniques of obstacle-avoidance (and their entailed sensing apparatus). This also decreases the necessary precision (and thus, presumably, the required degree of miniaturization) of the means of locomotion, which would need to be much greater if the unit were to perform real-time obstacle avoidance. Such tracks would be constructed in iterative fashion. The constructional system would analyze the space in front of it to determine if the space was occupied by a neuron terminal or soma, and extrude the tracks iteratively (e.g., add a segment in spaces where it detects the absence of biological material). It would then move along the newly extruded track, progressively extending it through the spaces between neurons as it moves forward.

Non-Distortional in vivo Brain “Scanning”

A novel avenue of enquiry that occurred during this period involves counteracting or taking into account the distortions caused by the data-measurement units on the elements or properties they are measuring and subsequently applying such corrections to the recording data. A unit changes the local environment that it is supposed to be measuring and recording, which becomes problematic. My solution was to test which operations performed by the units have the potential to distort relevant attributes of the neuron or its environment and to build units that compensate for it either physically or computationally.

If we reduce how a recording unit’s operation distorts neuronal behavior into a list of mathematical rules, we can take the recordings and apply mathematical techniques to eliminate or “cancel out” those distortions post-measurement, thus arriving at what would have been the correct data. This approach would work only if the distortions are affecting the recorded data (i.e., changing it in predictable ways), and not if they are affecting the unit’s ability to actually access, measure, or resolve such data.

The second approach applies the method underlying the first approach to the physical environment of the neuron. A unit senses and records the constituents of the area of space immediately adjacent to its edges and mathematically models that “layer”; i.e., if it is meant to detect ionic solutions (in the case of ECF or ICF), then it would measure their concentration and subsequently model ionic diffusion for that layer. It then moves forward, encountering another adjacent “layer” and integrating it with its extant model. By being able to sense iteratively what is immediately adjacent to it, it can model the space it occupies as it travels through that space. It then uses electric or chemical stores to manipulate the electrical and chemical properties of the environment immediately adjacent to its surface, so as to produce the emergent effects of that model (i.e., the properties of the edges of that model and how such properties causally affect/impact adjacent sections of the environment), thus producing the emergent effects that would have been present if the NRU-construction/integration system or data-measuring system hadn’t occupied that space.

The third postulated solution was the use of a grid comprised of a series of hollow recesses placed in front of the sensing/measuring apparatus. The grid is impressed upon the surface of the membrane. Each compartment isolates a given section of the neuronal membrane from the rest. The constituents of each compartment are measured and recorded, most probably via uptake of its constituents and transport to a suitable measuring apparatus. A simple indexing system can keep track of which constituents came from which grid (and thus which region of the membrane they came from). The unit has a chemical store operatively connected to the means of locomotion used to transport the isolated membrane-constituents to the measuring/sensing apparatus. After a given compartment’s constituents are measured and recorded, the system then marks its constituents (determined by measurement and already stored as recordings by this point of the process), takes an equivalent molecule or compound from a chemical inventory, and replaces the substance it removed for measurement with the equivalent substance from its chemical inventory. Once this is accomplished for a given section of membrane, the grid then moves forward, farther into the membrane, leaving the replacement molecules/compounds from the biochemical inventory in the same respective spots as their original counterparts. It does this iteratively, making its way through a neuron and out the other side. This approach is the most speculative, and thus the least likely to be used. It would likely require the use of NEMS, rather than MEMS, as a necessary technological infrastructure, if the approach were to avoid becoming economically prohibitive, because in order for the compartment-constituents to be replaceable after measurement via chemical store, they need to be simple molecules and compounds rather than sections of emergent protein or tissue, which are comparatively harder to artificially synthesize and store in working order.

***

In the next chapter I describe the work done throughout late 2009 on biological/non-biological NRU hybrids, and in early 2010 on one of two new approaches to retaining subjective-continuity through a gradual replacement procedure, both of which are unrelated to concerns of graduality or sufficient functional equivalence between the biological original and the artificial replication-unit.

The finished physical-functionalist units would need the ability to change their emergent morphology not only for active modification of single-neuron functionality but even for basic functional replication of normative neuron behavior, by virtue of needing to take into account neural plasticity and the way that morphological changes facilitate learning and memory. My original approach involved the use of retractable, telescopic dendrites and axons (with corresponding internal retractable and telescopic dendritic spines and axonal spines, respectively) activated electromechanically by the unit-CPU. For morphological changes, by providing the edges of each membrane section with an electromechanical hinged connection (i.e., a means of changing the angle of inclination between immediately adjacent sections), the emergent morphology can be controllably varied. This eventually developed to consist of an internal compartment designed so as to detach a given membrane section, move it down into the internal compartment of the neuronal soma or terminal, transport it along a track that stores alternative membrane sections stacked face-to-face (to compensate for limited space), and subsequently replaces it with a membrane section containing an alternate functional component (e.g., ion pump, ion channel, [voltage-gated or ligand-gated], etc.) embedded therein. Note that this approach was also conceived of as an alternative to retractable axons/dendrites and axonal/dendritic spines, by attaching additional membrane sections with a very steep angle of inclination (or a lesser inclination with a greater quantity of segments) and thereby creating an emergent section of artificial membrane that extends out from the biological membrane in the same way as axons and dendrites.

However, this approach was eventually supplemented by one that necessitates less technological infrastructure (i.e., that was simpler and thus more economical and realizable). If the size of the integral-membrane components is small enough (preferably smaller than their biological analogues), then differential activation of components or membrane sections would achieve the same effect as changing the organization or type of integral-membrane components, effectively eliminating the need at actually interchange membrane sections at all.

Active Neuronal Modulation and Modification

The technological and methodological infrastructure used to facilitate neural plasticity can also be used for active modification and modulation of neural behavior (and the emergent functionality determined by local neuronal behavior) towards the aim of mental augmentation and modification. Potential uses already discussed include mental amplification (increasing or augmenting existing functional modalities—i.e., intelligence, emotion, morality), or mental augmentation (the creation of categorically new functional and experiential modalities). While the distinction between modification and modulation isn’t definitive, a useful way of differentiating them is to consider modification as morphological changes creating new functional modalities, and to consider modulation as actively varying the operation of existing structures/processes through not morphological change but rather changes to the operation of integral-membrane components or the properties of the local environment (e.g., increasing local ionic concentrations).

Modulation: A Less Discontinuous Alternative to Morphological Modification

The use of modulation to achieve the effective results of morphological changes seemed like a hypothetically less discontinuous alternative to morphological changes (and thus as having a hypothetically greater probability of achieving subjective-continuity). I’m more dubious in regards to the validity of this approach now, because the emergent functionality (normatively determined by morphological features) is still changed in an effectively equivalent manner.

Upon full gradual replacement of the CNS with physical-functionalist equivalents, the preferred embodiment consisted of replacing the ionic solutions with electric fields that preserve the electric potential instantiated by the difference in ionic concentrations on the respective sides of the membrane. Such electric fields can be generated directly, without recourse to electrochemicals for manifesting them. In such a case the integral-membrane components would be replaced by a means of generating and maintaining a static and/or dynamic electric field on either side of the membrane, or even merely of generating an electrical potential (i.e., voltage—a broader category encompassing electric fields) with solid-state electronics.

This procedure would allow a fraction of the speedups (that is, increased rate of subjective perception of time, which extends to speed of thought) resulting from emulatory (i.e., strictly computational) replication-methods by no longer being limited to the rate of passive ionic diffusion—now instead being limited to the propagation velocity of electric or electromagnetic fields.

Wireless Synapses

If we replace the physical synaptic connections the NRU uses to communicate (with both existing biological neurons and with other NRUs) with a wireless means of synaptic-transmission, we can preserve the same functionality (insofar as it is determined by synaptic connectivity) while allowing any NRU to communicate with any other NRU or biological neuron in the brain at potentially equal speed. First we need a way of converting the output of an NRU or biological neuron into information that can be transmitted wirelessly. For cyber-physicalist-functionalist NRUs, regardless of their sub-class, this requires no new technological infrastructure because they already deal with 2nd-order (i.e., not structurally or directly embodied) information; informational-functional NRU deals solely in terms of this type of information, and the cyber-physical-systems sub-class of the physicalist-functionalist NRUs deal with this kind of information in the intermediary stage between sensors and actuators—and consequently, converting what would have been a sequence of electromechanical actuations into information isn’t a problem. Only the passive-physicalist-functionalist NRU class requires additional technological infrastructure to accomplish this, because they don’t already use computational operational-modalities for their normative operation, whereas the other NRU classes do.

We dispose receivers within the range of every neuron (or alternatively NRU) in the brain, connected to actuators – the precise composition of which depends on the operational modality of the receiving biological neuron or NRU. The receiver translates incoming information into physical actuations (e.g., the release of chemical stores), thereby instantiating that informational output in physical terms. For biological neurons, the receiver’s actuators would consist of a means of electrically stimulating the neuron and releasable chemical stores of neurotransmitters (or ionic concentrations as an alternate means of electrical stimulation via the manipulation of local ionic concentrations). For informational-functionalist NRUs, the information is already in a form it can accept; it can simply integrate that information into its extant model. For cyber-physicalist-NRUs, the unit’s CPU merely needs to be able to translate that information into the sequence in which it must electromechanically actuate its artificial ion-channels. For the passive-physicalist (i.e., having no computational hardware devoted to operating individual components at all, operating according to physical feedback between components alone) NRUs, our only option appears to be translating received information into the manipulation of the local environment to vicariously affect the operation of the NRU (e.g., increasing electric potential through manipulations of local ionic concentrations, or increasing the rate of diffusion via applied electric fields to attract ions and thus achieve the same effect as a steeper electrochemical gradient or potential-difference).

The technological and methodological infrastructure for this is very similar to that used for the “integrational NRUs”, which allows a given NRU-class to communicate with either existing biological neurons or NRUs of an alternate class.

Integrating New Neural Nets Without Functional Distortion of Existing Regions

The use of artificial neural networks (which here will designate NRU-networks that do not replicate any existing biological neurons, rather than the normative Artificial Neuron Networks mentioned in the first and second parts of this essay), rather than normative neural prosthetics and BCI, was the preferred method of cognitive augmentation (creation of categorically new functional/experiential modalities) and cognitive amplification (the extension of existing functional/experiential modalities). Due to functioning according to the same operational modality as existing neurons (whether biological or artificial-replacements), they can become a continuous part of our “selves”, whereas normative neural prosthetics and BCI are comparatively less likely to be capable of becoming an integral part of our experiential continuum (or subjective sense of self) due to their significant operational dissimilarity in relation to biological neural networks.

A given artificial neural network can be integrated with existing biological networks in a few ways. One is interior integration, wherein the new neural network is integrated so as to be “inter-threaded”, in which a given artificial-neuron is placed among one or multiple existing networks. The networks are integrated and connected on a very local level. In “anterior” integration, the new network would be integrated in a way comparable to the connection between separate cortical columns, with the majority of integration happening at the peripherals of each respective network or cluster.

If the interior integration approach is used then the functionality of the region may be distorted or negated by virtue of the fact that neurons that once took a certain amount of time to communicate now take comparatively longer due to the distance between them having been increased to compensate for the extra space necessitated by the integration of the new artificial neurons. Thus in order to negate these problematizing aspects, a means of increasing the speed of communication (determined by both [a] the rate of diffusion across the synaptic junction and [b] the rate of diffusion across the neuronal membrane, which in most cases is synonymous with the propagation velocity in the membrane – the exception being myelinated axons, wherein a given action potential “jumps” from node of Ranvier to node of Ranvier; in these cases propagation velocity is determined by the thickness and length of the myelinated sections) must be employed.

My original solution was the use of an artificial membrane morphologically modeled on a myelinated axon that possesses very high capacitance (and thus high propagation velocity), combined with increasing the capacitance of the existing axon or dendrite of the biological neuron. The cumulative capacitance of both is increased in proportion to how far apart they are moved. In this way, the propagation velocity of the existing neuron and the connector-terminal are increased to allow the existing biological neurons to communicate as fast as they would have prior to the addition of the artificial neural network. This solution was eventually supplemented by the wireless means of synaptic transmission described above, which allows any neuron to communicate with any other neuron at equal speed.

Gradually Assigning Operational Control of a Physical NRU to a Virtual NRU

This approach allows us to apply the single-neuron gradual replacement facilitated by the physical-functionalist NRU to the informational-functionalist (physically embodied) NRU. A given section of artificial membrane and its integral membrane components are modeled. When this model is functioning in parallel (i.e., synchronization of operative states) with its corresponding membrane section, the normative operational routines of that artificial membrane section (usually controlled by the unit’s CPU and its programming) are subsequently taken over by the computational model—i.e., the physical operation of the artificial membrane section is implemented according to and in correspondence with the operative states of the model. This is done iteratively, with the informationalist-functionalist NRU progressively controlling more and more sections of the membrane until the physical operation of the whole physical-functionalist NRU is controlled by the informational operative states of the informationalist-functionalist NRU. While this concept sprang originally from the approach of using multiple gradual-replacement phases (with a class of model assigned to each phase, wherein each is more dissimilar to the original than the preceding phase, thereby increasing the cumulative degree of graduality), I now see it as a way of facilitating sub-neuron gradual replacement in computational NRUs. Also note that this approach can be used to go from existing biological membrane-sections to a computational NRU, without a physical-functionalist intermediary stage. This, however, is comparatively more complex because the physical-functionalist NRU already has a means of modulating its operative states, whereas the biological neuron does not. In such a case the section of lipid bilayer membrane would presumably have to be operationally isolated from adjacent sections of membrane, using a system of chemical inventories (of either highly concentrated ionic solution or neurotransmitters, depending on the area of membrane) to produce electrochemical output and chemical sensors to accept the electrochemical input from adjacent sections (i.e., a means of detecting depolarization and hyperpolarization). Thus to facilitate an action potential, for example, the chemical sensors would detect depolarization, the computational NRU would then model the influx of ions through the section of membrane it is replacing and subsequently translate the effective results impinging upon the opposite side to that opposite edge via either the release of neurotransmitters or the manipulation of local ionic concentrations so as to generate the required depolarization at the adjacent section of biological membrane.

Integrational NRU

This consisted of a unit facilitating connection between emulatory (i.e., informational-functionalist) units and existing biological neurons. The output of the emulatory units is converted into chemical and electrical output at the locations where the emulatory NRU makes synaptic connection with other biological neurons, facilitated through electric stimulation or the release of chemical inventories for the increase of ionic concentrations and the release of neurotransmitters, respectively. The input of existing biological neurons making synaptic connections with the emulatory NRU is read, likewise, by chemical and electrical sensors and is converted into informational input that corresponds to the operational modality of the informationalist-functionalist NRU classes.

Solutions to Scale

If we needed NEMS or something below the scale of the present state of MEMS for the technological infrastructure of either (a) the electromechanical systems replicating a given section of neuronal membrane, or (b) the systems used to construct and/or integrate the sections, or those used to remove or otherwise operationally isolate the existing section of lipid bilayer membrane being replaced from adjacent sections, a postulated solution consisted of taking the difference in length between the artificial membrane section and the existing bilipid section (which difference is determined by how small we can construct functionally operative artificial ion-channels) and incorporating this as added curvature in the artificial membrane-section such that its edges converge upon or superpose with the edges of the space left by the removal the lipid bilayer membrane-section. We would also need to increase the propagation velocity (typically determined by the rate of ionic influx, which in turn is typically determined by the concentration gradient or difference in the ionic concentrations on the respective sides of the membrane) such that the action potential reaches the opposite end of the replacement section at the same time that it would normally have via the lipid bilayer membrane. This could be accomplished directly by the application of electric fields with a charge opposite that of the ions (which would attract them, thus increasing the rate of diffusion), by increasing the number of open channels or the diameter of existing channels, or simply by increasing the concentration gradient through local manipulation of extracellular and/or intracellular ionic concentration—e.g., through concentrated electrolyte stores of the relevant ion that can be released to increase the local ionic concentration.

If the degree of miniaturization is so low as to make this approach untenable (e.g., increasing curvature still doesn’t allow successful integration) then a hypothesized alternative approach was to increase the overall space between adjacent neurons, integrate the NRU, and replace normative connection with chemical inventories (of either ionic compound or neurotransmitter) released at the site of existing connection, and having the NRU (or NRU sub-section—i.e., artificial membrane section) wirelessly control the release of such chemical inventories according to its operative states.

The next chapter describes (a) possible physical bases for subjective-continuity through a gradual-uploading procedure and (b) possible design requirements for in vivo brain-scanning and for systems to construct and integrate the prosthetic neurons with the existing biological brain.